Spatial patterns of retail stores using POIs data in Zhengzhou, China

Cui, Caihui; Han, Zhigang

doi:10.1109/icsdm.2015.7298031

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…Integrating this aspect into the analysis would require limiting data manipulation procedures that lead to the manifestation of survivorship bias. Beyond the manual removal of zeros, smoothing/interpolation procedures such as kernel density estimation (KDE) [8][9][10][18][19][20] also result in similar conclusions. In this case, the relationship between the variables under analysis is overstated [24] (p. 63), valuable and detailed information such as an absence of stores might be omitted or diluted and the autocorrelation component of micro-retail distribution is artificially amplified.…”

Section: Stores Absence and The Survivorship Biasmentioning

confidence: 96%

“…Pearson's correlation represents the most implemented analytical procedure [8][9][10][18][19][20]. This approach allows the presence of a linear relationship between two continuous variables to be evaluated.…”

Section: Analysing the Relationship Between Urban Form And Micro-reta...mentioning

confidence: 99%

“…Three main solutions might be considered: (i) implementation of a rank correlation test (i.e., Spearman, Kendall); (ii) evaluation of the non-linear fit between micro-retail and urban form properties [16,21]; and (iii) manipulations of the original data to meet a normality assumption such as log-transformation or, similarly, smoothing approaches [8][9][10][18][19][20]. Independently from the specific solution, these procedures propose simple bivariate analysis, assessing strength and direction of the relationship between each morphological variable and micro-retail measures.…”

Section: Analysing the Relationship Between Urban Form And Micro-reta...mentioning

confidence: 99%

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Towards an Integrated Methodology for Model and Variable Selection Using Count Data: An Application to Micro-Retail Distribution in Urban Studies

Araldi¹

2020

Urban Science

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Over the last two decades, a growing number of works in urban studies have revealed how micro-retail distribution is significantly related to specific properties of the urban built environment. While a wide variety of urban form measures have been investigated using sophisticated analytical approaches, the same attention has not equally been found in statistical procedures. Several essential features of micro-retail statistical distribution and modelling assumptions are frequently overlooked, compromising the statistical robustness of outcomes. In this work we focus on four main aspects: (i) the discrete, non-negative and highly skewed nature of store distribution; (ii) its zero-inflation; (iii) assessment of the contextual effect; and (iv) the multicollinearity generated by the inclusion of highly related urban descriptors. To overcome these limitations, we propose an integrated methodological framework for both modelling and variable selection assessment based on generalized linear models (GLMs) and elastic-net (Enet) penalized regression (PR), respectively. The procedure is tested via a real case study of the French Riviera, which is described using a large dataset of 105 street-based urban form measures. The outcomes of this procedure show the superiority of the zero-inflate negative binomial count regression approach. A restricted number of urban form properties are found to be related to the micro-retail distribution depending on the specific scale and morphological context under analysis.

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Section: Stores Absence and The Survivorship Biasmentioning

confidence: 96%

Section: Analysing the Relationship Between Urban Form And Micro-reta...mentioning

confidence: 99%

Section: Analysing the Relationship Between Urban Form And Micro-reta...mentioning

confidence: 99%

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Towards an Integrated Methodology for Model and Variable Selection Using Count Data: An Application to Micro-Retail Distribution in Urban Studies

Araldi¹

2020

Urban Science

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“…In particular, Han, et al (2019) identified the spatial patterns of retail stores in the road network structure of the Chinese city of Zhengzhou using points of interests (POIs) by applying the Network-Based Kernel Density Estimation (NKDE) and employing the global, local, and weighted closeness centrality. Cui and Han (2015) investigated the spatial patterns of retail stores in Zhengzhou using POIs by applying the Standard Deviational Ellipse (SDE), the Average Nearest Neighbor index and the Kernel Density Estimation (KDE), while they quantified the centrality of nodes of urban streets using the indices of betweenness centrality, closeness centrality and straightness centrality. Wu, et al (2018) examined the spatial distribution pattern of enterprises in Beijing and their influencing factors by applying the KDE, the Kernel-Nearest Neighbor (KNN) and the Vector analysis theory on landscape pattern (VATLP).…”

Section: Introductionmentioning

confidence: 99%

Spatial point pattern analysis of urban retail stores: the case of twelve large- and medium-sized Greek cities

TSOUTSOS¹,

Photis²

2020

EJG

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The retailers’ profitability and the consumers’ satisfaction depend on finding the optimal location for a retail store. When considering the stores’ spatial distribution, business potential can be understood and a squandering planning of resources can be avoided. In this paper we identify the spatial patterns of retail stores located in the traditional commercial centers of twelve large -and medium-sized Greek cities, aiming to explain why such patterns exist. The type of retail activities was determined using the image of the ground-floor stores provided by the Google Street View (GSV) service and thus 7322 stores were recorded in a geodatabase as point features. The results reveal that the retail stores’ distribution has a clustered and random spatial pattern at least in one city, where the high population density and the increase in rental prices of premises for professional activities constitute the factors that form these spatial patterns respectively.

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“…This potential is amplified when the network is provided with additional information about the vertices and edges (i.e., weights). Through specialized structures, such as weighted graphs, the related literature has centered on improving the urban design [Goh et al 2016], cities comprehension [Cui and Han 2015], and human behavior modeling ; mainly in the face of heavy traffic [Iacus et al 2020], epidemics [Kraemer et al 2020], and criminality ]. These problems reveal the intricacies underneath the human behavior [Song et al 2010, Souza 2017], demonstrating why these phenomena are challenging to understand when one looks from a single perspective.…”

mentioning

confidence: 99%

From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics

Spadon¹

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Spatial patterns of retail stores using POIs data in Zhengzhou, China

Cited by 10 publications

References 8 publications

Towards an Integrated Methodology for Model and Variable Selection Using Count Data: An Application to Micro-Retail Distribution in Urban Studies

Towards an Integrated Methodology for Model and Variable Selection Using Count Data: An Application to Micro-Retail Distribution in Urban Studies

Spatial point pattern analysis of urban retail stores: the case of twelve large- and medium-sized Greek cities

From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics

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